High Vegetation Density Research Articles

Tradeoffs between soil conservation and soil‐water retention: The role of vegetation pattern and density

AbstractBalancing erosion control and the water conservation service is the primary goal of intensive revegetation in water‐limited conditions. In this study, a field experiment was conducted in the dryland Chinese Loess Plateau (CLP), to investigate the role of vegetation pattern and density in their tradeoff between soil conservation (SC) and soil water retention (SWR). Five vegetation patterns (up‐slope covered, downslope covered, low and high uniformly covered, no coverage) with three levels of grass density (220 g m−2, 110 g m−2, and 0) were designed and implemented. A novel indicator EEW (the exchange between erosion reduction and water consumption), as the cost of any extra depletion of soil water to improve each unit of soil conservation, was used to represent the tradeoff during a complete dry‐wet‐dry soil water convert cycle. The higher EEW, the poorer tradeoff between SC and SWR. It was illustrated that higher vegetation density could promote SC but not inevitably suppress EEW. The present typical grass density in CLP, round 220 g m−2, was redundant in optimizing the SC‐SWR tradeoff. A concentrated and centralized vegetation distribution mainly in the downslope obtained an EEW at 11.6 mm m2 kg−1 and was confirmed as a favoured vegetation pattern. The simulated typical density and spatial pattern in CLP (about 220 g m−2 and uniformly distribution) had EEW at 20.5 mm m2 kg−1, by reducing the density to about 110 g m−2 and a downslope centralized vegetation redistribution, the EEW was reduced by 43.4%. Under long‐term drought conditions at dry‐wet‐dry cycles, soil water decreased more sharply in the Full‐covered plots, due to more evapotranspiration and vertical water loss caused by higher plant density, compared with half‐covered and bare soil plots. The present study confirmed that planted density and vegetation pattern can manipulate SC‐SWR tradeoff and it was thus possible to optimize it by adjusting revegetation's intensity and structures in practice.

Monsoon-phase regulates the decoupling of auto- and heterotrophic respiration by mediating soil nutrient availability and root biomass in tropical grassland

Soil respiration is considered as one of the most sensitive parameters regulated by the interplay of different soil biophysical parameters, ground cover and climatic conditions. As per the global predictions, monsoon variability in South Asian countries can affect various soil processes. However, studies exploring the interactive role of vegetation and precipitation conditions on soil respiration and its components (viz., auto- and heterotrophic) are lacking. A mini-trenching method was performed to explore the alterations in auto- and heterotrophic components of soil respiration as well as their regulatory variables in grassland vegetation under different summer monsoonal phases. Three vegetation densities (viz. low, medium and high) based on herbaceous cover, and three monsoon phases (viz. onset, active and end) of the tropical summer monsoon were considered. The findings revealed that heterotrophic respiration was the major contributor to the total soil respiration under medium- and high-density vegetation with percent contribution of 62–80% and 53–88% respectively. Contrastingly, autotrophic respiration was the major contributor (54–56%) to soil respiration in low-density vegetation during active- and monsoon-end phases. Amongst the three monsoon phases, variation in soil respiration components was most pronounced during the active-monsoon phase. Structural Equation Modelling results revealed microbial biomass and root biomass as key regulators of auto- and heterotrophic respiration. Remarkably, a partitioning of root biomass parallel to the soil respiration was observed during monsoon-end phase where coarse root biomass and fine root biomass emerged as regulators of auto- and heterotrophic respiration, respectively. Moreover, for monsoon-onset and -end phases, a completely different set of variables involved in regulation of soil respiration components were identified. Our results revealed that the monsoon-phase mediates the root and nutrient dynamics which led to the differential decoupling of the components of soil respiration. Thus, inclusion of (below)-ground cover and monsoon-phase wise variation in soil biophysical parameters in the global biogeochemical models would be more helpful for better prediction of carbon balance in different ecosystems.

Application of earth observation dataset and multi-criteria decision-making technique for forest fire risk assessment in Sikkim, India

Forest fire is one of the primary and recurring problems in Sikkim, India impacting the ecological heritage of the region. The article presents a fire risk model based on the identification of the major factors that contribute to forest fire, namely, vegetation type, vegetation density, land surface temperature, elevation, slope, aspect, and distance from settlements, rivers and roads, and then integrating them using a multi-criteria decision-making technique in a GIS framework. We document that more than 50% of the area of all the districts except North Sikkim falls into high to moderate risk zones. The model shows that 61% of fire information for resource management system data for the last 16 years coincide with the mapped high-risk zone of the state. Areas with low slope and with moderate vegetation density fall into very high risk, whereas areas with high slope and with high vegetation density correspond to moderate risk zones. Further, aspect and density of human intervention differentiate the very high and high-risk zones of the region. This model has provided a robust geographical representation of fire ignition probability and identification of high-risk areas at different regions for the entire state of Sikkim

Electroencephalography (EEG)-Based Neural Emotional Response to the Vegetation Density and Integrated Sound Environment in a Green Space

Emotion plays an important role in physical and mental health. Green space is an environment conducive to physical and mental recovery and influences human emotions through visual and auditory stimulation. Both the visual environment and sound environment of a green space are important factors affecting its quality. Most of the previous relevant studies have focused solely on the visual or sound environment of green spaces and its impacts. This study focused on the combination of vegetation density (VD) and integrated sound environment (ISE) based on neural emotional evaluation criteria. VD was used as the visual variable, with three levels: high (H), moderate (M) and low (L). ISE was used as the sound variable, with four levels: low-decibel natural and low-decibel artificial sounds (LL), low-decibel natural and high-decibel artificial sounds (LH), high-decibel natural and low-decibel artificial sounds (HL) and high-decibel natural and high-decibel artificial sounds (HH). These two variables were combined into 12 unique groups. A total of 360 volunteer college students were recruited and randomly assigned to the 12 groups (N = 30). All 12 groups underwent the same 5 min high-pressure learning task (pretest baseline), followed by a 5 min audio-visual recovery (posttest). Six indicators of neural emotion (engagement, excitement, focus, interest, relaxation and stress) were dynamically measured by an Emotiv EPOC X device during the pretest and posttest. Analysis of covariance was used to determine the main and coupled effects of the variables. (1) VD and ISE have significant effects on human neural emotions. In moderate- and high-VD spaces, artificial sound levels may have a positive effect on excitement. (2) A higher VD is more likely to result in excitatory neural emotion expression. (3) Low-VD and high-VD spaces have a higher degree of visual continuity. Both extremely low and extremely high VDs result in a higher expression of stressful emotions than observed for a moderate VD. (4) High-decibel artificial sounds are more likely to attract attention, possibly because artificial sounds are easier to recognize than natural sounds. However, when both the natural and artificial sounds are low, it is difficult to induce higher tones, and the lower the artificial sound decibel level, the easier it is to relax. Additionally, under the influence of an ISE, attention recovery and stress recovery may be negatively correlated. The results show that an appropriate combination of VD and ISE can improve the health benefits of a green space and thus the well-being of visitors.

The role of soil, water and forest conservation on vegetation cover and landscape greenness in degraded areas of upper Blue Nile

Recently, large-scale soil, water, and forest conservation/protection practices have been implemented in Ethiopia. This study aimed to evaluate the impact of soil, water, and forest conservation/protection on vegetation cover and landscape greenness in the Gumara watershed. Landsat satellite imageries were used to analyze the changes using land use/cover and net difference vegetation index (NDVI) and its differencing methods. Over the period 1995-2017, forestland and shrub-woodland covers increased by 48.4% (2.8-4.2%) and 8.3% (20.2-21.9%), respectively. Similarly, the NDVI result showed high improvements in landscape greenness and vegetation density. A 13.5% of the watershed area experienced a significant increase, of which 61.4% was observed on forest and shrub-woodland covers. The watershed area covered by very high (NDVI>0.4) and high (NDVI 0.3-0.4) vegetation density classes were increased by 189.2 and 145.5%, respectively. Upon the increasing human pressure and related problems, the observed improvement of vegetation cover and landscape greenness show the positive impact of soil, water, and forest conservation/protection practices done for the last two decades. Therefore, strengthening the current efforts and investing more in sustainable and evidence/priority-based soil, water, and forest conservation measures that are ecologically friendly with diversified livelihood importance can bring a more effective result of land rehabilitation.

Vertical distribution characteristics of particulate matter beside an elevated expressway by unmanned aerial vehicle measurements

Increasing knowledge in the law of particulate matter (PM) distributions beside elevated expressways could assist in addressing the deterioration of urban air quality. However, the vertical distribution patterns of PM concentration have not been clearly recognized due to limited measurement methods. Hence, unmanned aerial vehicle (UAV) measurements are carried out at three typical sites near an elevated expressway to investigate the effects of the noise barrier, vegetation, and building on PM vertical distribution patterns. Results show that the order of mean PM concentration in three trial sites from low to high is: 86.3 μg/m3 at VN (with vegetation and noise barrier), 90.2 μg/m3 at CT (control, without vegetation, noise barrier, and building), and 93.2 μg/m3 at VNB (with vegetation, noise barrier, and building) site. Additionally, the PM1 concentrations present a unimodal distribution, peaking at 5–7 m and 7–9 m at CT and VN sites, respectively. PM10 concentrations decrease continuously with height at CT site and show a bimodal distribution at VN site, with the maximum and the upper peak occurring at 0–3 m and 5–7 m, respectively. High-density vegetation is more conducive to remove PM downwind than low-density vegetation in the open road, while the opposite is true in the street canyon. Moreover, statistically significant positive correlations are obtained between roadside concentration and traffic volume, as well as between roadside concentration and background concentration, with Spearman correlation coefficients of 0.61 and 0.93, respectively. This study could provide insights in mitigating traffic-related PM pollution into urban areas.

Production of Urban Space and the occurrence of malaria in the Brazilian Amazon: the Porto Velho case.

Due to intense ongoing urbanization in the Amazon, the urban pattern of malaria may be changing, both in its spatial distribution and epidemiological profile. The purpose of this paper is to analyze how the process of production of urban space in Porto Velho, the capital of the state of Rondonia, Brazil has contributed to the occurrence and maintenance of urban malaria. Using data collected from the Malaria Epidemiological Surveillance System (SIVEP-Malaria), we calculated malaria indices for the districts of Porto Velho from 2005 to 2018. We also developed two typologies for classifying urban space based on functional characteristics and features of the landscape. While the former considers characteristics of urban space in Porto Velho, the latter is based on suitability for malaria vectors. We found that the annual parasite index declined in Porto Velho during the study period. However, changes in the index were not uniform across the districts of the city. Periurban areas showed no decline in the index, which we attribute to these areas' high vegetation density and hydrological characteristics.

Bryophytes of Rio Turvo State Park (SP), Brazil: integrating floristics, geographical distribution, reproduction and ecological traits to support the conservation of an Atlantic Forest fragment

Rio Turvo State Park (RTSP) is a Conservation Unit located within the Atlantic Forest biome in the Vale do Ribeira region of the State of São Paulo, Brazil. Considering the lack of knowledge regarding its bryoflora, we performed a floristic inventory of RTSP (following the random-walk method), the results of which we report here including discussion of the geographical distribution (Brazil and worldwide), substrate colonization, life forms and reproduction of the species. We found a total of 414 species, which represents 30 % of all known bryophyte species in Brazil. Dioicous species were predominant over monoicous species, the most colonized substrate was tree trunk (40 % of all species) and the predominant life form was mat (47 %), indicating that the floristic composition is influenced by the environmental conditions of the area (high humidity, dense vegetation and shading). Although anthropogenic intervention is present in RTSP, it harbors huge biodiverse potential, since 22 % of the species are rare. Furthermore, 16 new occurrences were recorded for the State of São Paulo. The information provided by this study exalts the importance of this Conservation Unit in the preservation of bryophytes, as well as all the biodiversity of its included ecosystems.

Biogeochemical and Hydrological Variables Synergistically Influence Nitrate Variability in Coastal Deltaic Wetlands

AbstractCoastal river deltas are centers of surface water nitrate processing, yet the mechanisms controlling spatio‐temporal patterns in nutrient variability are still little understood. Nitrate fluctuations in these systems are controlled by complex interactions between hydrological and biogeochemical drivers, which act together to transport and transform inorganic nutrients. Distinguishing the contributions of these drivers and identifying wetland zones where nitrate processing is occurring can be difficult, yet is critical to make assessments of nutrient removal capacity in deltaic wetlands. To address these issues, we analyze relationships among regional “external” (river discharge, tides, wind) and local “internal” (water level, temperature, turbidity, and nitrate) variables in a deltaic wetland in coastal Louisiana by coupling a process connectivity framework with information theory measures. We classify variable interactions according to whether they work uniquely, redundantly, or synergistically to influence nitrate dynamics and identify timescales of interaction. We find that external drivers work together to influence nitrate transport. Patterns of hydrological and sediment connectivity change over time due to tidal flushing and discharge variation. This connectivity influences the emergence of functional zones where local nitrate fluctuations and temperature and water level process couplings are strong controls on nitrate variability. High vegetation density decreases hydrological process connectivity, even during periods of high river discharge, but it also increases biogeochemical process connections, due to the lengthening of the hydraulic residence time. Based on these results we make recommendations for monitoring nitrate in a wetland.

PENGARUH KARAKTERISTIK HABITAT MANGROVE TERHADAP KEPADATAN KEPITING ( SCYLLA SERRATA ) DI PANTAI UTARA KABUPATEN DEMAK, JAWA TENGAH

Rehabilitation of mangrove forests is very important to maintain ecosystem stability and increase the productivity of mangrove forests. The success of mangrove forest rehabilitation can be seen by the density of mangrove vegetation and the increase in mangrove crabs. Mangrove crab is the main commodity for farming communities because in addition to playing an important role in the mangrove ecosystem, it also has high economic value so it needs to be developed to increase people's income. This research was conducted with the aim of knowing the density of mangrove vegetation and crab density in different planting years and the effect of mangrove habitat characteristics on crab density. The research was conducted in three different planting years, namely 2003, 2005, and 2006. Each planting year consisted of a front zone, a middle zone and a back zone. In each zone a measuring plot with a size of 5 m x 5 m was made to collect data on vegetation density, temperature, mud thickness, salinity, acidity, and dissolved oxygen (DO). Then in the 5 m x 5 m plot, a 1 m x 1 m plot was made to collect data on the number of crabs. Data analysis used regression equation and ANOVA difference test. The results showed that the highest vegetation density was in the back zone of the 2006 planting year with a density of 2160 individuals/ha, while the highest density of crabs was in the 2005 planting year with a density of 12111.11 individuals/ha. Regression analysis showed the effect of temperature, mud thickness and salinity variables on crab density with the equation Y = 3.447 - 0.044X1 - 0.009X2 - 0.230X3 with R = 0.809. In the different ANOVA test, only the thickness of the mud had a significant difference at three years of planting (0.00 < 0.05).

Flow resistance of floodplain vegetation mixtures for modelling river flows

River flows are greatly influenced by floodplain vegetation with implications on hydrological and hydraulic conditions from cross-sectional to river reach scales. Flow models need to reliably reflect changes in the riverine environment, such as vegetation growth associated with altered flow regimes, increased sediment loads and eutrophication. Leaf area index (LAI) based approaches are increasingly used as tools to predict the flow resistance caused by natural vegetation. However, current LAI-based modelling involves uncertainty at low and high vegetation densities and flow velocities due to a lack of research and validation at the outer ranges. The aim of this paper is to investigate the flow resistance for a mixture of flexible floodplain vegetation consisting of woody plants and understory grasses of low to high densities (LAI = 1–5) over a wide range of mean flow velocities (0.05–1.2 m/s) at low relative submergences of 1–2. A novel flume setup was designed by using high-accuracy pressure sensors to measure flow resistance and force sensors to measure plant drag forces. Flow resistance decreased by 35–90% when the submergence H/hv increased from 1 to 2, which is a highly relevant range for floodplain flows. The results provided new evidence that LAI-based modelling of vegetative friction factors can be reliably extended from low to high LAI values for non-submerged vegetation. However, adjustments to existing LAI-based approaches are required for water stages higher than the vegetation height. Furthermore, the friction by the understory grasses cannot be neglected as often assumed in literature especially for cases of low plant densities and low relative submergences.

Synergistic Influence of Local Climate Zones and Wind Speeds on the Urban Heat Island and Heat Waves in the Megacity of Beijing, China

Large-scale modifications to urban underlying surfaces owing to rapid urbanization have led to stronger urban heat island (UHI) effects and more frequent urban heat wave (HW) events. Based on observations of automatic weather stations in Beijing during the summers of 2014–2020, we studied the interaction between HW events and the UHI effect. Results showed that the UHI intensity (UHII) was significantly aggravated (by 0.55°C) during HW periods compared to non-heat wave (NHW) periods. Considering the strong impact of unfavorable weather conditions and altered land use on the urban thermal environment, we evaluated the modulation of HW events and the UHI effect by wind speed and local climatic zones (LCZs). Wind speeds in urban areas were weakened due to the obstruction of dense high-rise buildings, which favored the occurrence of HW events. In detail, 35 HW events occurred over the LCZ1 of a dense high-rise building area under low wind speed conditions, which was much higher than that in other LCZ types and under high wind speed conditions (< 30 HW events). The latent heat flux in rural areas has increased more due to the presence of sufficient water availability and more vegetation, while the increase in heat flux in urban areas is mainly in the form of sensible heat flux, resulting in stronger UHI effect during HW periods. Compared to NHW periods, lower boundary layer and wind speed in the HW events weakened the convective mixing of air, further expanding the temperature gap between urban and rural areas. Note that LCZP type with its high-density vegetation and water bodies in the urban park area generally exhibited, was found to have a mitigating effect on the UHI, whilst at the same time increasing the frequency and duration of HW events during HW periods. Synergies between HWs and the UHI amplify both the spatial and temporal coverage of high-temperature events, which in turn exposes urban residents to additional heat stress and seriously threatens their health. The findings have important implications for HWs and UHII forecasts, as well as for scientific guidance on decision-making to improve the thermal environment and to adjust the energy structure.

Contrasting edge and pasture matrix effects on ant diversity from fragmented landscapes across multiple spatial scales

Harmful effects of habitat loss and fragmentation can be detected across multiple spatial scales, yet most studies that aim to characterize these effects take place at a single spatial scale. Here we investigated responses in ant diversity to forest fragmentation across three spatial scales. We sampled ant diversity in a fragmented landscape in southeastern Brazil. We set transects with 15 pitfall traps (local scale response) within 16 forest fragments (fragment scale response) distributed in eight paired fragments separated by roads (mesoscale response). We measured habitat features at local, fragment and mesoscales and across the landscape. For each spatial scale, we then assessed the effects of ecological drivers measured at the same and subsequent higher spatial scales. Local diversity was higher with closer proximity to the matrix, and in fragments with high vegetation density. Both fragment species richness and β-diversity were higher in less circular fragments. However, at the mesoscale (pairs of fragments), total species richness decreased with increasing pasture matrix in the landscape (landscape driver). Edge and matrix effects lead to increased ant species richness at small spatial scales (due to an increase in generalist ants). However, when analyzed at a higher spatial scale, the responses to these effects were in fact reversed, leading to a decrease in ant diversity. Thus, we suggest that conservation efforts should take a multi-scale perspective (from local to landscape) and aim to protect both local and fragment characteristics that can buffer edge effects, as well as improve matrix quality in the landscape.

Vegetation index-based biomass model and Land Surface Temperature (LST) from urban green spaces in Bandung City derived from multispectral imageries

Bandung City is the largest metropolitan city in West Java Province, with a population of more than 2.5 million people in 2019. With the vast number of population, the need for land that is inevitable. The need for land in this area continuously increased, but the balanced is not meet with the availability of land. Further, it will encourage the conversion of natural land such as green spaces into a built-up form such as settlements. A disturbed vegetation cover would have an impact on the carbon sequestration capabilities and, on the other hand, might increase the land surface temperature. The goals of this study are to determine the carbon sequestration capabilities of tree biomass in urban green space and to examine the relationship between biomass content and land surface temperature (LST). Some vegetation indices include NDVI, ARVI, and SAVI, derived from multispectral Sentinel 2B are being correlated with allometric-based biomass value to generate biomass estimation models. Spatial analysis is useful to provide information about the value of biomass in the tree canopy and spatial distribution of biomass in the City of Bandung. It results that,NDVI provides a better correlation than SAVI and ARVI. The amount of Biomass in the UGS vegetation in Bandung City is4098.504 ton.Spatial distribution of high-value biomass is located in the north of Bandung, while low-value biomass is spread spatially in the south of Bandung. Large-sized trees with high vegetation density are in areas with high biomass values.While LST values range from 0 to 34.73°C. If there is a correlation between biomass and LST, it produces an inverse correlation of 0.33 with a moderate relationship.

Vegetation cover analysis and ecotourism business model for sustainable forest management in Gunung Leuser National Park, Indonesia

Gunung Leuser National Park (GLNP) is part of the Tropical Rainforest Heritage program of Sumatra that was designated by UNESCO. GLNP is very vulnerable to vegetation land cover decrease due to illegal logging, encroachment, oil palm expansion, and inappropriate management of ecotourism. GLNP authority has the responsibility to handle forest threats by estimating vegetation cover and manage the ecotourism sector. This study aims to estimate vegetation cover and investigate strategy in developing ecotourism business model for sustainable forest management in GLNP. The methods used Normalized Vegetation Index (NDVI) analysis in estimating vegetation cover, and Social Enterprise - Business Model Canvas (SE-BMC) in developing a strategic business ecotourism model. NDVI was classified into 5 classes from class 1 to 5 (non-vegetation or very low dense vegetation to very high dense vegetation) based on unsupervised classification (natural breaks in ArcGIS). The results showed the largest vegetation area in GLNP belongs to class 4 (445876 ha or 32.68%) and class 5 (355018 ha or 26.02%). This indicates that the majority of GLNP by area is still densely vegetated. Based on SE-BMC analysis, GLNP management needs to collaborate with the local community and other stakeholders. We recommend improving ecotourism management and enhancing community development.

Environmental Correlates of Lyme Disease Emergence in Southwest Virginia, 2005-2014.

Lyme disease is the most common tick-borne disease in North America. Though human infection is mostly transmitted in a limited geography, the range has expanded in recent years. One notable area of recent expansion is in the mountainous region of southwestern Virginia. The ecological factors that facilitate or constrain the range of human Lyme disease in this region remain uncertain. To evaluate this further, we obtained ecological data, including remotely sensed data on forest structure and vegetation, weather data, and elevation. These data were aggregated within the census block groups of a 9,153 km2 area around the cities of Blacksburg and Roanoke, VA, an area with heterogeneous Lyme disease transmission. In this geographic area, 755 individuals were reported to have Lyme disease in the 10 yr from 2006 to 2015, and these cases were aggregated by block group. A zero-inflated negative binomial model was used to evaluate which environmental variables influenced the abundance of Lyme disease cases. Higher elevation and higher vegetation density had the greatest effect size on the abundance of Lyme disease. Measures of forest edge, forest integrity, temperature, and humidity were not associated with Lyme disease cases. Future southward expansion of Lyme disease into the southeastern states may be most likely in ecologically similar mountainous areas.

Detection of potential green open space area using landsat 8 satellite imagery

Green city development policies have an essential role in improving the ecological functions of a municipality. According to Law 26/2007 on spatial planning, it contains provinsions for green open space which oblige the government to set aside 30% of the city as green open space. However, the main obstacle in providing green open space remains the limited availability of urban land. Therefore, efforts are needed to detect urban land that is available for use as green open space. The purpose of this study is to determine the initial description of the availability of green open space in Malang City. This study uses an integrated method of calculating the Normalized Difference Vegetation Index (NDVI) with land cover classification. NDVI analysis reveals the distribution value of vegetation density in Malang City. Based on NDVI analysis results, green open space requires medium and high density vegetation. In Malang City, only 2946.56 ha, or 26.60% of the city, meets these requirements. The availability of green open space in Malang City therefore does not meet minimum green open space requirements. This study's results suggest the need for additional policies aimed at providing green open space for Malang City.

Reconstruction of ESA CCI satellite-derived soil moisture using an artificial neural network technology

Soil moisture (SM) is a vital variable for controlling water and heat exchange between the atmosphere and land surface. Spatiotemporally continuous satellite-derived SM information is urgently needed for large-scale meteorological and hydrological applications. This study proposes the application of a spatial gap-filling method using an artificial neural network (ANN) to reconstruct missing records of daily surface SM from the Climate Change Initiative program of the European Space Agency (ESA CCI) in the growing seasons of 1982–2015 across China. Ten environmental variables were taken into consideration, including meteorological forcing, geographic and topographic features, vegetation conditions, and soil texture. The ANN-reconstructed SM and RF-reconstructed SM were validated using the ground-based observations and three global reanalysis SM datasets. The gap-filling results by the ANN model were compared to that by the original kriging (OK) model under three simulation scenarios: SM images with removed swaths, varying vegetation densities, and different percentages of data gaps. The results showed that the ANN-reconstructed SM was in good agreement with in-situ SM observations, with the CC more than 0.607 and the RMSE less than 0.074 m3/m3 over the northwestern and eastern parts of China. Compared with the original ESA CCI SM, the ANN model performed better than the OK model in reconstructing SM with absent swaths and in densely and sparsely vegetated regions. Specifically, the ANN-reconstructed SM provided a higher estimation accuracy in regions with low-density vegetation than in those with high-density vegetation. The weaker performance may due to the complex interactions between surface SM and various environmental variables underneath the dense vegetation. In addition, the ANN model significantly outperformed the OK model by accurately estimating SM when the percentage of data gaps succeeded 40%. Our study is valuable for providing an alternative reference to reconstruct satellite-derived SM and highlights the potential of using the ANN model.

Normalized Difference Vegetation Index (NDVI) analysis for vegetation cover in Leuser Ecosystem area, Sumatra, Indonesia

Abstract. Hartoyo APP, Sunkar A, Ramadani R, Faluthi S, Hidayati S. 2021. Normalized Difference Vegetation Index (NDVI) analysis for vegetation cover in Leuser Ecosystem area, Sumatra, Indonesia. Biodiversitas 22: 1160-1171. About 2 Mha of 24.3 Mha degraded area in Indonesia is inside conservation area. The Leuser Ecosystem Area (LEA) is the largest conservation area in Malesian forest that plays essential role in biodiversity and ecosystem services conservation efforts. It is the last habitat on earth where Sumatran tigers, elephants, orangutans, and rhinoceros are found together. However, LEA faces many threats, such as infrastructure development, and industrial palm oil plantation. Additionally, vegetation cover data as an approach to monitor forest cover changes in LEA is still lacking and baseline data regarding composition, structure as well as vegetation diversity in LEA is very limited. The objectives of this study were to analyze vegetation cover using Normalized Difference Vegetation Index (NDVI) in LEA and its relation to agroforestry structure, composition and diversity in Agusen Village, Gayo Lues District and Alur Durin Village, East Aceh District, Aceh Province belonging to LEA. Based on the NDVI analysis, the largest area in LEA belonged to class 5, meaning that the most area in LEA was dominated by high dense vegetation (1,870,116.40 ha). The average accuracy and standard error of NDVI analysis were 83.33% and 2.62. LEA is an effective buffer for maintaining forest ecosystems and increasing the local communities' welfare through agroforestry system. Agroforestry structures in agroforestry practices, both in Agusen Village and Alur Durin Village did not reflect reverse-J curve, meaning that enrichment planting for increasing numbers of individual and species was necessary. Management of agroforestry system depends on the landowners or managers and their selection of shade tree species with high economic value with market demand such as C. arabica, T. cacao with A. moluccanus, L. leucocephala, H. brasiliensis, D. zibethinus, etc. Trees that produce non-timber products are also an alternative way for conservation strategy and sustainable utilization.

Detection of seasonal changes in vegetation and morphology on coastal salt marshes using terrestrial laser scanning

Coastal salt marshes exhibit strong seasonal variations that may induce subsequent changes in their morphology. However, such changes are insufficiently understood due to a lack of data at seasonal timescales. Knowledge of the seasonal variability is essential for coastal management and ecosystem conservation. Terrestrial laser scanning (TLS) technology allows the rapid acquisition of high-resolution and large-scale vegetation and morphology data with an error of <3 cm. In this study, we used a TLS system to retrieve high-accuracy digital canopy models (DCM) for low vegetation species (approximately 20 cm) Suaeda salsa and digital terrain models (DTM) of a marsh in the Yellow River Delta. We conducted three TLS surveys on the same field on January 5, April 21, and November 20, 2019, which spanned from the dry to wet seasons. The centimeter-scale TLS data retrieved the seasonal variations of vegetation and elevation distributions. The DCMs of the study area showed that the percentages of vegetation-occupied regions were 13.5% and 22.5% in the dry and wet seasons, respectively. The DTMs indicated a net erosion of the study area throughout the year, but at a larger erosion rate in the dry than the wet season, that is, a rate of 2.5 and 0.2 cm/month, respectively. Spatially, regions with vegetation and zones adjacent to creeks underwent slight deposition. We inferred that the larger riverine sediment supply and higher vegetation density in the wet (summer) season mitigated erosion, while the influence of rich sediment supply was crucial. This work demonstrates the applicability of TLS in detecting the distribution of low-height vegetation species, and quantifying vegetation and morphological changes of marshes at seasonal timescales owing to its high resolution.